SKILLEMALL.ai

AC self-improving-agent

Capture errors, corrections, and recurring patterns into structured `.learnings/` logs, then promote durable guidance into workspace memory files. Use when commands fail, users correct output, missing capabilities are requested, or new best practices should be preserved across sessions.

ClawHub Agent Skills author: ollieb89 v1.1.0 16 files · 4 scripts body ≈ 418 tokens Open the sourceclawhub.ai analyzed 2 d ago

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI and agentsInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 16. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 51/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (self-improving-agent) differs from the folder (self-improving-agent-ollieb89)
    • 100Tools and files. No external tools needed
    • 100Steps. 18 steps
    • 100Execution cost. Instruction body is 418 tokens
    • 100Running it twice. No mutating operations
    • 100Progress reporting. Reports progress

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 287: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 18 items
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (3 of 3)
    • +3All 4 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.

    External checks

    ClawHub: suspicious
    This skill appears intended for agent learning, but it documents broad always-on hooks and persistent memory updates that can affect many sessions and projects.
    LLM: suspicious (medium) · VirusTotal: · 29 May 2026